Digital services

CAF’s new digital platform, LeadMind, offers a new generation of connected trains which provides more competitive services for operators and maintainers in the railway industry by data compilation, storage, processing and analysis.

LeadMind is a modular, open and scalable product which can be tailored to meet customers’ requirements. Data is provided in a user-friendly format, offering a tool to facilitate the decision-making process.

Fleet Health Condition

Display of the general fleet condition, including vehicle-specific alarms.
Support for maintainers and operators when it comes to prioritising maintenance activities and detecting the causes of each alarm.
The purpose of the system is to provide a clear and visual picture of the entire fleet in real time such that the control station can make swift and effective decisions.

Geolocation

This enables a rapid view of availability and alarms of the fleet to choose the right moment to carry out maintenance, thereby reducing train downtime.
Specific areas can be swiftly defined and detected on the fleet Geolocation screen (e.g. warehouses, stations, loading terminals), as these are critical points.
A filter can be applied to the fleet trains to reduce the train search time and to speed up decision making.

Remote HMI

This allows for the real-time viewing of the information displayed to the driver.
This real-time viewing provides a complete display of the vehicle screens, including vehicle alarms.
As the control room has access to all this information in real time, a team of remotely-located specialists are ready to offer optimal operating support, enabling the train driver to make fast and effective decisions.

Remote Condition Monitoring

This displays a subset of the variables received in real time to the user.
These variables are hierarchically ordered based on the train unit, system and car. Variables can also be verified on the same train unit or between different train units in the fleet.

Descriptive Analysis

Time series variables over time allow the user to select variables to analyse performance over time to detect patterns. The purpose of this function is to detect the aforementioned patterns.
The effect of variables on other variables can be checked by means of autocorrelation analysis. This is applicable, for example, when temperature is correlated with poor performance over time with the resulting alarms.

Energy efficiency

The tool provides various graphs to maximise energy/fuel efficiency and to control driver performance. This has been realised by integrating monitoring tools associated with the power accumulation systems, air consumption, infrastructure, rolling gear, etc.
The energy efficiency tool enables the customer to constantly analyse consumption of the various train systems: traction, auxiliaries, catenary, HVAC, etc.
The tool also reduces energy and fuel costs by calculating the optimum driving patterns and providing the driver with real time suggestions.

CBM (Condition Based Maintenance)

The CBM tool displays the status of the fleet based on the various life indicators in order to improve the following:

Analysis of the root cause and performance faults.
Analysis of maintenance costs.
Analysis of the effectiveness of maintenance tasks.
Maintenance intervals and strategies based on available data.